The present application generally relates to data mining. More particularly, the present application relates to mining a generalized spatial association rule using data mining software.
Spatial association rule (SAR) mining finds rules describing frequent patterns of spatial relationships between spatial objects. Krzysztof Koperski and Jiawei Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proceedings of the 4th International Symposium on Advances in Spatial Databases SSD '95, wholly incorporated by reference as if set forth herein, hereinafter “Koperski,” describes a standard SAR mining algorithm in detail. A spatial relationship is defined between a reference spatial object and a task-relevant spatial object, and is represented by a spatial predicate describing spatial information of the reference spatial object. The standard SAR mining algorithm can also be extended by adding a taxonomy handler to mine rules with concepts of spatial objects. However, the standard SAR mining algorithm can only discover rules that associate the spatial information (e.g., adjacency, etc.) of reference spatial objects. The discovered rules through the standard SAR mining algorithm cannot include non-spatial information (e.g., demographic information, etc.) of either reference or task-relevant spatial objects in generated rules.
FIG. 1 illustrates traditional ways of creating generalized rules (e.g., inferences, etc.) that associates two or more items, e.g., shirt, jacket, hiking boots, etc, and/or their taxonomies (i.e., concept classifications). There is provided a table 100 that stores prior transactions of purchased items, e.g., shirt, jacket, hiking boots, etc. There is provided a taxonomy 105 of each item involved in the prior transactions. A traditional rule miner with a taxonomy handler for creating generalized association rules expands the table 100 by using the taxonomy 105. Srikant, et al. “Mining Generalized Association Rules,” Proceeding of the 21st VLDB Conference, Zurich, Switzerland, 1995, wholly incorporated by reference as if set forth herein, hereinafter “Srikant,” describes a traditional rule miner with a taxonomy hander. Han, et al., “Discovery of Multiple-Level Association Rules from Large Databases,” Proceedings of the 21st VLDB Conference, Zurich, Switzerland, 1995, wholly incorporated by reference as if set forth herein, hereinafter “Han,” describes another traditional rule miner with a taxonomy handler. For example, as shown in an expanded table 110, the transaction 200 is expanded to include “outerwears” and “clothes” which are more generic classes of “jacket.” The transaction 200 is further expanded to include “footwear” which is a higher class of “hiking boots.” Based on the expanded table 110, the traditional rule miner creates a table 115. For example, since “Jacket” appears twice in the expanded table 110, the corresponding support value of the “Jacket” in table 115 is two. Based on the table 115, the traditional rule miner creates generalized association rules (e.g., outerwear→hiking boots). Thus, the traditional rule miner may infer that there is a correlation between the “outerwear” and “hiking boots.” For example, the rule “outerwear→hiking boots” has 33% support value and 66.6% confidence value. The support value of the generalized association rule (e.g., outerwear→hiking boots) is the percentage of the transactions that include both outerwear and hiking boots. The confidence value of the generalized association rule refers that 66.6% of customers who purchase outerwear also purchase hiking boots.
However, the traditional rule miner without taxonomy handler cannot generate rules that associate concepts with items, e.g., “outerwear” and “hiking boots.” The rules generated from the traditional rule miner do not include other information, for example, price, place of the purchase, etc.